Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells3
Missing cells (%)0.2%
Duplicate rows44
Duplicate rows (%)44.0%
Total size in memory12.6 KiB
Average record size in memory129.3 B

Variable types

NUM15
CAT1

Warnings

Dataset has 44 (44.0%) duplicate rows Duplicates
HOMA_IR is highly correlated with FIHigh correlation
FI is highly correlated with HOMA_IRHigh correlation
FI has 3 (3.0%) missing values Missing
GENSINI has 36 (36.0%) zeros Zeros
SYNTAX has 36 (36.0%) zeros Zeros

Reproduction

Analysis started2020-10-24 17:49:33.025790
Analysis finished2020-10-24 17:50:17.682043
Duration44.66 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Age
Real number (ℝ≥0)

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.09
Minimum39
Maximum77
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:17.828840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile41.85
Q146.75
median58
Q364
95-th percentile71
Maximum77
Range38
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation9.867910446
Coefficient of variation (CV)0.175929942
Kurtosis-1.061994876
Mean56.09
Median Absolute Deviation (MAD)7
Skewness-0.1286114305
Sum5609
Variance97.37565657
MonotocityNot monotonic
2020-10-24T23:20:18.023839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%) 
4299.0%
 
6188.0%
 
7166.0%
 
4566.0%
 
3955.0%
 
5855.0%
 
6455.0%
 
6055.0%
 
6255.0%
 
5544.0%
 
Other values (18)4242.0%
 
ValueCountFrequency (%) 
3955.0%
 
4299.0%
 
4322.0%
 
4566.0%
 
4633.0%
 
ValueCountFrequency (%) 
7711.0%
 
7211.0%
 
7166.0%
 
7011.0%
 
6944.0%
 

BMI
Real number (ℝ≥0)

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.1412
Minimum17.3
Maximum40.3
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:18.171509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum17.3
5-th percentile19.37
Q121.6
median24
Q326.3
95-th percentile29.645
Maximum40.3
Range23
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation3.984764236
Coefficient of variation (CV)0.1650607359
Kurtosis3.780118411
Mean24.1412
Median Absolute Deviation (MAD)2.4
Skewness1.35576879
Sum2414.12
Variance15.87834602
MonotocityNot monotonic
2020-10-24T23:20:18.340746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
2099.0%
 
25.377.0%
 
21.977.0%
 
2455.0%
 
21.655.0%
 
24.655.0%
 
22.144.0%
 
28.4933.0%
 
18.833.0%
 
24.6733.0%
 
Other values (27)4949.0%
 
ValueCountFrequency (%) 
17.322.0%
 
18.833.0%
 
19.411.0%
 
2099.0%
 
20.233.0%
 
ValueCountFrequency (%) 
40.322.0%
 
31.2522.0%
 
30.511.0%
 
29.622.0%
 
29.522.0%
 

WC
Real number (ℝ≥0)

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.13
Minimum72
Maximum106
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:18.510270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile74
Q180
median88
Q392
95-th percentile102
Maximum106
Range34
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.321391443
Coefficient of variation (CV)0.09550546819
Kurtosis-0.5032147113
Mean87.13
Median Absolute Deviation (MAD)5
Skewness0.09703923543
Sum8713
Variance69.24555556
MonotocityNot monotonic
2020-10-24T23:20:18.637869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
881414.0%
 
8077.0%
 
9277.0%
 
9466.0%
 
9066.0%
 
8455.0%
 
10255.0%
 
7555.0%
 
8344.0%
 
9144.0%
 
Other values (15)3737.0%
 
ValueCountFrequency (%) 
7244.0%
 
7422.0%
 
7555.0%
 
7644.0%
 
7722.0%
 
ValueCountFrequency (%) 
10622.0%
 
10255.0%
 
99.533.0%
 
9933.0%
 
9811.0%
 

HbA1c
Real number (ℝ≥0)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.643
Minimum4.4
Maximum9.5
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:18.777069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile5.3
Q15.7
median6
Q37.55
95-th percentile9.4
Maximum9.5
Range5.1
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation1.271065329
Coefficient of variation (CV)0.191339053
Kurtosis-0.3365825932
Mean6.643
Median Absolute Deviation (MAD)0.55
Skewness0.8188642139
Sum664.3
Variance1.615607071
MonotocityNot monotonic
2020-10-24T23:20:18.934136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
61212.0%
 
5.988.0%
 
5.788.0%
 
5.477.0%
 
7.466.0%
 
5.855.0%
 
6.155.0%
 
5.344.0%
 
9.544.0%
 
5.644.0%
 
Other values (19)3737.0%
 
ValueCountFrequency (%) 
4.422.0%
 
5.344.0%
 
5.477.0%
 
5.511.0%
 
5.644.0%
 
ValueCountFrequency (%) 
9.544.0%
 
9.433.0%
 
8.922.0%
 
8.422.0%
 
8.322.0%
 

FPG
Real number (ℝ≥0)

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.34
Minimum63
Maximum220
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:19.088678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile74
Q186
median95
Q3110
95-th percentile170
Maximum220
Range157
Interquartile range (IQR)24

Descriptive statistics

Standard deviation31.56433866
Coefficient of variation (CV)0.3025142674
Kurtosis2.803878143
Mean104.34
Median Absolute Deviation (MAD)12.5
Skewness1.690535013
Sum10434
Variance996.3074747
MonotocityNot monotonic
2020-10-24T23:20:19.287562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
961111.0%
 
8899.0%
 
8677.0%
 
7877.0%
 
10855.0%
 
9255.0%
 
8144.0%
 
9444.0%
 
13033.0%
 
16433.0%
 
Other values (23)4242.0%
 
ValueCountFrequency (%) 
6322.0%
 
6422.0%
 
7422.0%
 
7877.0%
 
8144.0%
 
ValueCountFrequency (%) 
22022.0%
 
17322.0%
 
17022.0%
 
16822.0%
 
16433.0%
 

TC
Real number (ℝ≥0)

Distinct36
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.01
Minimum108
Maximum257
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:19.447054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile117.75
Q1159
median175
Q3184.5
95-th percentile250
Maximum257
Range149
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation35.96954239
Coefficient of variation (CV)0.2055284977
Kurtosis-0.04077019828
Mean175.01
Median Absolute Deviation (MAD)15.5
Skewness0.309763856
Sum17501
Variance1293.80798
MonotocityNot monotonic
2020-10-24T23:20:19.654238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
18299.0%
 
16888.0%
 
25055.0%
 
17455.0%
 
16444.0%
 
13944.0%
 
14044.0%
 
17733.0%
 
18033.0%
 
18433.0%
 
Other values (26)5252.0%
 
ValueCountFrequency (%) 
10833.0%
 
11322.0%
 
11822.0%
 
12022.0%
 
12433.0%
 
ValueCountFrequency (%) 
25722.0%
 
25055.0%
 
22922.0%
 
22633.0%
 
22033.0%
 

TG
Real number (ℝ≥0)

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.64
Minimum79
Maximum459
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:19.814576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile91.65
Q1109
median141
Q3184
95-th percentile232.25
Maximum459
Range380
Interquartile range (IQR)75

Descriptive statistics

Standard deviation61.46009956
Coefficient of variation (CV)0.3974398575
Kurtosis10.3688992
Mean154.64
Median Absolute Deviation (MAD)35
Skewness2.48295178
Sum15464
Variance3777.343838
MonotocityNot monotonic
2020-10-24T23:20:20.033546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%) 
14166.0%
 
16766.0%
 
9666.0%
 
13455.0%
 
17844.0%
 
13744.0%
 
14333.0%
 
18433.0%
 
10933.0%
 
21333.0%
 
Other values (32)5757.0%
 
ValueCountFrequency (%) 
7911.0%
 
8122.0%
 
8522.0%
 
9211.0%
 
9322.0%
 
ValueCountFrequency (%) 
45922.0%
 
23733.0%
 
23222.0%
 
22922.0%
 
22311.0%
 

HDL
Real number (ℝ≥0)

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.95
Minimum27
Maximum61
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:20.201213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile28
Q132
median40
Q344.25
95-th percentile48.25
Maximum61
Range34
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation7.42963626
Coefficient of variation (CV)0.1907480426
Kurtosis-0.0002441439737
Mean38.95
Median Absolute Deviation (MAD)6
Skewness0.3528285666
Sum3895
Variance55.19949495
MonotocityNot monotonic
2020-10-24T23:20:20.374323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%) 
421212.0%
 
4699.0%
 
4588.0%
 
3088.0%
 
4477.0%
 
3966.0%
 
3266.0%
 
4066.0%
 
3555.0%
 
2955.0%
 
Other values (13)2828.0%
 
ValueCountFrequency (%) 
2733.0%
 
2833.0%
 
2955.0%
 
3088.0%
 
3111.0%
 
ValueCountFrequency (%) 
6122.0%
 
5333.0%
 
4822.0%
 
4711.0%
 
4699.0%
 

GROUP
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size800.0 B
1
47 
4
23 
3
15 
2
15 
ValueCountFrequency (%) 
14747.0%
 
42323.0%
 
31515.0%
 
21515.0%
 
2020-10-24T23:20:20.557407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T23:20:20.655229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:20.774437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

LDL
Real number (ℝ≥0)

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.96
Minimum44
Maximum183
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:20.935828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile50.9
Q190
median106.5
Q3115.5
95-th percentile160
Maximum183
Range139
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation31.26876649
Coefficient of variation (CV)0.3036981982
Kurtosis0.4327552843
Mean102.96
Median Absolute Deviation (MAD)14
Skewness0.3229643164
Sum10296
Variance977.7357576
MonotocityNot monotonic
2020-10-24T23:20:21.083658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%) 
9366.0%
 
11155.0%
 
9655.0%
 
12144.0%
 
11544.0%
 
11444.0%
 
10944.0%
 
6644.0%
 
9744.0%
 
13233.0%
 
Other values (28)5757.0%
 
ValueCountFrequency (%) 
4422.0%
 
4933.0%
 
5122.0%
 
5522.0%
 
5633.0%
 
ValueCountFrequency (%) 
18322.0%
 
18222.0%
 
16033.0%
 
14833.0%
 
13422.0%
 

CH_HDL
Real number (ℝ≥0)

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6162
Minimum2.5
Maximum7.8
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:21.275007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile2.7
Q13.76
median4.2
Q35.325
95-th percentile6.92
Maximum7.8
Range5.3
Interquartile range (IQR)1.565

Descriptive statistics

Standard deviation1.318280775
Coefficient of variation (CV)0.2855770493
Kurtosis-0.2950377105
Mean4.6162
Median Absolute Deviation (MAD)0.7
Skewness0.6066581643
Sum461.62
Variance1.737864202
MonotocityNot monotonic
2020-10-24T23:20:21.430558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
41111.0%
 
4.266.0%
 
666.0%
 
2.755.0%
 
5.355.0%
 
3.655.0%
 
344.0%
 
4.444.0%
 
3.544.0%
 
4.144.0%
 
Other values (23)4646.0%
 
ValueCountFrequency (%) 
2.522.0%
 
2.755.0%
 
2.822.0%
 
344.0%
 
3.111.0%
 
ValueCountFrequency (%) 
7.833.0%
 
7.322.0%
 
6.922.0%
 
6.833.0%
 
6.433.0%
 

FI
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct47
Distinct (%)48.5%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean35.62917526
Minimum4.68
Maximum216
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:21.598256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4.68
5-th percentile4.87
Q112.4
median25.92
Q338.52
95-th percentile136.6
Maximum216
Range211.32
Interquartile range (IQR)26.12

Descriptive statistics

Standard deviation40.1986656
Coefficient of variation (CV)1.128251364
Kurtosis8.799140711
Mean35.62917526
Median Absolute Deviation (MAD)13.52
Skewness2.785708406
Sum3456.03
Variance1615.932716
MonotocityNot monotonic
2020-10-24T23:20:21.766270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%) 
4.8755.0%
 
7.3444.0%
 
27.4144.0%
 
62.5933.0%
 
4.6833.0%
 
28.2933.0%
 
33.1433.0%
 
23.4533.0%
 
136.633.0%
 
16.0533.0%
 
Other values (37)6363.0%
 
ValueCountFrequency (%) 
4.6833.0%
 
4.7711.0%
 
4.8755.0%
 
5.133.0%
 
6.211.0%
 
ValueCountFrequency (%) 
21622.0%
 
17111.0%
 
136.633.0%
 
77.2133.0%
 
75.0822.0%
 

HOMA_IR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2704
Minimum0.9
Maximum39.5
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:21.958415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1
Q12.7
median5.5
Q310.315
95-th percentile33.11
Maximum39.5
Range38.6
Interquartile range (IQR)7.615

Descriptive statistics

Standard deviation9.914538444
Coefficient of variation (CV)1.06948335
Kurtosis2.307646232
Mean9.2704
Median Absolute Deviation (MAD)3.6
Skewness1.767936258
Sum927.04
Variance98.29807257
MonotocityNot monotonic
2020-10-24T23:20:22.273647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%) 
1.777.0%
 
7.966.0%
 
1.155.0%
 
144.0%
 
4.533.0%
 
5.533.0%
 
3.233.0%
 
2.633.0%
 
2.733.0%
 
9.133.0%
 
Other values (32)6060.0%
 
ValueCountFrequency (%) 
0.922.0%
 
144.0%
 
1.155.0%
 
1.522.0%
 
1.777.0%
 
ValueCountFrequency (%) 
39.522.0%
 
37.133.0%
 
32.911.0%
 
31.333.0%
 
25.822.0%
 

hsCRP
Real number (ℝ≥0)

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8842
Minimum0.5
Maximum38.7
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-10-24T23:20:22.514439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.6
Q11.1
median1.9
Q33.3
95-th percentile16.8
Maximum38.7
Range38.2
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation6.96693695
Coefficient of variation (CV)1.793660715
Kurtosis17.73741729
Mean3.8842
Median Absolute Deviation (MAD)0.965
Skewness4.138119214
Sum388.42
Variance48.53821046
MonotocityNot monotonic
2020-10-24T23:20:22.690669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
1.488.0%
 
0.866.0%
 
2.866.0%
 
1.766.0%
 
0.655.0%
 
2.144.0%
 
3.444.0%
 
1.144.0%
 
244.0%
 
1.333.0%
 
Other values (25)5050.0%
 
ValueCountFrequency (%) 
0.511.0%
 
0.655.0%
 
0.722.0%
 
0.866.0%
 
0.8933.0%
 
ValueCountFrequency (%) 
38.733.0%
 
21.911.0%
 
16.822.0%
 
7.322.0%
 
6.822.0%
 

GENSINI
Real number (ℝ≥0)

ZEROS

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.84
Minimum0
Maximum125
Zeros36
Zeros (%)36.0%
Memory size800.0 B
2020-10-24T23:20:22.839857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q327
95-th percentile56
Maximum125
Range125
Interquartile range (IQR)27

Descriptive statistics

Standard deviation23.5617478
Coefficient of variation (CV)1.487484079
Kurtosis7.633189788
Mean15.84
Median Absolute Deviation (MAD)4
Skewness2.371269023
Sum1584
Variance555.1559596
MonotocityNot monotonic
2020-10-24T23:20:22.989089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
03636.0%
 
299.0%
 
455.0%
 
544.0%
 
2744.0%
 
3933.0%
 
2233.0%
 
4033.0%
 
5633.0%
 
3033.0%
 
Other values (16)2727.0%
 
ValueCountFrequency (%) 
03636.0%
 
299.0%
 
2.522.0%
 
311.0%
 
455.0%
 
ValueCountFrequency (%) 
12522.0%
 
5922.0%
 
5633.0%
 
5211.0%
 
4622.0%
 

SYNTAX
Real number (ℝ≥0)

ZEROS

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.295
Minimum0
Maximum38
Zeros36
Zeros (%)36.0%
Memory size800.0 B
2020-10-24T23:20:23.125300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q315
95-th percentile34.1
Maximum38
Range38
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.83323893
Coefficient of variation (CV)1.165491009
Kurtosis0.7065427451
Mean9.295
Median Absolute Deviation (MAD)6
Skewness1.223712263
Sum929.5
Variance117.3590657
MonotocityNot monotonic
2020-10-24T23:20:23.265103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%) 
03636.0%
 
788.0%
 
1577.0%
 
555.0%
 
244.0%
 
433.0%
 
1333.0%
 
3833.0%
 
1033.0%
 
933.0%
 
Other values (13)2525.0%
 
ValueCountFrequency (%) 
03636.0%
 
244.0%
 
322.0%
 
433.0%
 
555.0%
 
ValueCountFrequency (%) 
3833.0%
 
3622.0%
 
3422.0%
 
32.522.0%
 
22.533.0%
 

Interactions

2020-10-24T23:19:40.771793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:40.979235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:41.118840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:41.285717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:41.422344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:41.562925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:41.699068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:41.843127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:41.981501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:42.130346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:42.273252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:42.402492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:42.541113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:42.679977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:42.817370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:42.949956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:43.077419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:43.195147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:43.308812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:43.419167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:43.545365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:43.682215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:43.808700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:43.938372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:44.055496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:44.202806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:44.426113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:44.551046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:44.669166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:44.779894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:44.893937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:45.018626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:45.134560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:45.255334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:45.367851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:45.531953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:45.664536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:45.819867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:45.958948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:46.090728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:46.218939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:46.339799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:46.484617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:46.651991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:46.800264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:46.932399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:47.050911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:47.164188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:47.284787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:47.402112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:47.540492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:47.674532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:47.842145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:47.971277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:48.096952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:48.211540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:48.333820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:48.546478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:48.702771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:48.855090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:49.014874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:49.173294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:49.522524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:49.668312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:49.795839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:49.980757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:50.191446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:50.333534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:50.465490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:50.645624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:50.819387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:50.947389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:51.090104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:51.296983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:51.433801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:51.579679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:51.763141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:51.909170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:52.048719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:52.178046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:52.377623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:52.541894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:52.725856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:52.914555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:53.101643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:53.260542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:53.404326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:53.561312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:53.738884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:53.928214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:54.108248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:54.246509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:54.369278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:54.498632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:54.623245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:54.764192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:54.904250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:55.042798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:55.172735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:55.307686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:55.434003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:55.554111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:55.696591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:55.841690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:56.204763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:56.399781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:56.576948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:56.711819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:56.892837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:57.064878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:57.267587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:57.461362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:57.628404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:57.773618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:57.904484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:58.036897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:58.155718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:58.294826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:58.424477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:58.550095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:58.681367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:58.812851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:58.939952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:59.083811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:59.206780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:59.353562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:59.489517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:59.625113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:59.760410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:19:59.901313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:00.094182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:00.246859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:00.392713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:00.538849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:00.688696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:00.816304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:00.945367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:01.107139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:01.279160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:01.408176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:01.539774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:01.712230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:01.905480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:02.040989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:02.196762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:02.352241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:02.466948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:02.644219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:02.796548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-24T23:20:09.703761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:09.923187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:10.163147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:10.384299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:10.536486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:10.665480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:10.777426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:10.902616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:11.095867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:11.308296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:11.512245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:11.679587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:11.832676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:12.016401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:12.200090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:12.394595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:12.579936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:12.795531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:12.963062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:13.116756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:13.291480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:13.508655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:13.749456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:13.938627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:14.089057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:14.236429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:14.453934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:14.647336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:14.814350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:14.990570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:15.475460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:15.674340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:15.860459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:16.030525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-24T23:20:23.421601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-24T23:20:24.076565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-24T23:20:24.371011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-24T23:20:24.637295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-10-24T23:20:16.570031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:17.071812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T23:20:17.466304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

AgeBMIWCHbA1cFPGTCTGHDLGROUPLDLCH_HDLFIHOMA_IRhsCRPGENSINISYNTAX
06220.0080.05.4882261084411605.15.102.62.1056.022.5
15421.9082.04.483178157341964.24.871.01.4038.015.0
26418.80102.06.1921842372721106.84.871.10.8922.038.0
35231.25106.05.896150127614642.516.954.00.702.57.0
46040.30102.06.3116168167424934.029.638.51.324.07.0
56024.6788.05.9941801842811156.0NaN0.90.970.00.0
64220.0088.06.096168167421934.07.341.72.000.00.0
74822.2082.05.694220975311484.24.681.11.300.00.0
85822.1076.05.6158166132392944.16.207.41.000.00.0
94228.4991.05.781108143303493.616.053.21.404.010.0

Last rows

AgeBMIWCHbA1cFPGTCTGHDLGROUPLDLCH_HDLFIHOMA_IRhsCRPGENSINISYNTAX
903924.6099.57.41642501413211327.877.2131.35.902.03.0
916125.6092.06.0961771414031094.433.147.90.8030.019.0
925621.6072.05.4902131764511214.724.425.43.405.04.0
934623.5094.08.116815985451973.562.2025.82.6046.017.0
946420.0090.07.91062501164511825.625.756.71.1020.015.0
956418.80102.06.1921842372721106.84.871.10.8922.038.0
964220.0088.06.096168167421934.07.341.72.000.00.0
974228.4991.05.781108143303493.616.053.21.404.010.0
987120.2075.07.4108182213292966.462.5916.70.9027.015.0
996320.9078.06.010811381421552.720.535.55.2010.022.0

Duplicate rows

Most frequent

AgeBMIWCHbA1cFPGTCTGHDLGROUPLDLCH_HDLFIHOMA_IRhsCRPGENSINISYNTAXcount
24220.0088.06.096168167421934.07.341.72.000.00.04
44228.4991.05.781108143303493.616.053.21.404.010.03
155825.3084.08.213014096354864.028.299.138.7039.013.03
176121.9076.05.7781821863021176.023.454.53.302.05.03
196125.6092.06.0961771414031094.433.147.90.8030.019.03
206220.0080.05.4882261084411605.15.102.62.1056.022.53
236418.80102.06.1921842372721106.84.871.10.8922.038.03
317120.2075.07.4108182213292966.462.5916.70.9027.015.03
327128.2092.09.4110124109464562.7136.6037.12.8040.016.03
03922.1077.05.398169135421974.113.803.31.100.00.02